def test_QuartznetBlock_device_move(**kwargs):
try:
block = QuartznetBlock(**kwargs)
except ValueError:
return
x = torch.randn(10, kwargs["in_channels"], 1337)
lens = torch.randint(10, 1337, (10, ))
_test_device_move(block, (x, lens))
def test_normalize_device_move():
norm = FeatureBatchNormalizer()
x = torch.randn(10, 40, 1337)
_test_device_move(norm, x)
def test_filterbank_device_move(**kwargs):
fb = FilterbankFeatures(**kwargs)
x = torch.randn(10, 1337)
# Relaxed tolerance because of log operation
# inside the Melscale
_test_device_move(fb, x, atol=1e-3)
def test_melscale_device_move(**kwargs):
mel = MelScale(**kwargs)
x = torch.randn(10, int(1 + kwargs["n_fft"] // 2), 137).abs()
_test_device_move(mel, x)
def test_powerspectrum_device_move(**kwargs):
spec = PowerSpectrum(**kwargs)
x = torch.randn(10, 1337)
_test_device_move(spec, x)
def test_preemph_filter_device_move(preemph):
filt = PreEmphasisFilter(preemph)
x = torch.randn(10, 1337)
_test_device_move(filt, x)
def test_dither_device_move(dither_magnitude):
dither = DitherAudio(dither_magnitude)
x = torch.randn(10, 1337)
_test_device_move(dither, x)